Higher education % in Oregon - Alis volat propriis
Welcome to my notebook covering the use of the tidycensus R package for extracting and evaluating census data in the state of Oregon. We are interested in uncovering the percentages of the population with graduate level degrees at a county level.
Start by importing the list of libraries that will assist us in connecting with the Census Bureau’s APIs and visualizing the desired data.
library(tidycensus) # connect with the census bureau API
library(tidyverse) # data wrangling
library(mapview) # spatial data visualizer
library(plotly) # graph / chart visualizer
library(ggiraph) # graph / chart visualizer
library(ggplot2)
library(DT)
library(scales)
library(sf)We will use the get_acs() function from tidycensus package to pull data from the census bureau. The function defaults to the 2017-2021 5-year ACS dataset. Which is necessary for us, since there are a list of counties in Oregon with a population of less than 65,000.
# Data objects needed
or_grads <- get_acs(
geography = "county",
variables = "DP02_0066P",
state = "OR",
year = 2021,
)
or_grads_table <- or_grads %>%
separate(NAME, into = c("county", "state"), sep = ", ") %>%
arrange(-estimate) %>%
select(county, estimate, moe)We have our results in a table and we can filter through to see all counties with their percent of population with graduate degrees. Lets use the datatable() function from the DT library to provide a bit more interactivity for previewing the results.
# function from the DT library
datatable(or_grads_table)Top 3 counties –
- Benton
- Multnomah
- Washington
Bottom 3 counties –
- Malheur
- Gilliam
- Morrow
or_plot <- ggplot(or_grads_table, aes(x = estimate,
y = reorder(county, estimate))) +
geom_point(color="navy", size = 3) +
scale_x_continuous(labels = function(x) paste0(x, '%')) +
scale_y_discrete(labels = function(x) str_remove(x, " County")) +
labs(title = "% Population with graduate degrees, 2017-2021 ACS",
subtitle = "Counties in Oregon",
caption = "Data acquired with R and tidycensus",
x = "ACS estimate",
y = "") +
theme_minimal(base_size = 12)
or_plot
or_plot_errorbar <- ggplot(or_grads, aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe),
width = 0.5, linewidth = 0.5) +
geom_point(color = "gold", size = 3) +
scale_x_continuous(labels = function(x) paste0(x, '%')) +
scale_y_discrete(labels = function(x) str_remove(x, " County, Oregon|, Oregon")) +
labs(title = "% Population with graduate degrees, 2017-2021 ACS",
subtitle = "Counties in Oregon",
caption = "Data acquired with R and tidycensus. Error bars represent margin of error around estimates.",
"ACS estimate",
y = "",
x = "Percentage") +
theme_minimal(base_size = 12)
or_plot_errorbar
or_income <- get_acs(
geography = "county",
variables = "B19013A_001",
state = "OR",
year = 2021,
geometry = TRUE
)Getting data from the 2017-2021 5-year ACS
Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '41' for state 'OR'
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|=====================================================================================| 100%or_house <- get_acs(
geography = "county",
variables = "B25105_001",
state = "OR",
year = 2021,
geometry = TRUE
)Getting data from the 2017-2021 5-year ACS
Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Using FIPS code '41' for state 'OR'ggplotly(or_plot_errorbar, tooltip = "x")NA# Data objects needed
or_grads_geom <- get_acs(
geography = "county",
variables = "DP02_0066P",
state = "OR",
year = 2021,
geometry = TRUE
)
ggplot() + # use ggplot to create the plot.
geom_sf(data = or_grads_geom, # use geom_sf to plot the polyline geometry.
aes(fill = as.factor(estimate)),
linewidth = 0.4) + # color based on year.
scale_color_discrete(name = "estimate") + # set color scale
coord_sf(datum = NA) + # put geometries on the same coordinate reference system
theme_void()# median salary for computer programmers across the country
#income_variables <- "B24121_067"
# populatiion for computer programmers
#pop_variables <- "B24114_067"
renter_bachelors<- get_acs(
geography = "tract",
variables = "B25013_011",
state = "OR",
year = 2021,
geometry = TRUE
)Getting data from the 2017-2021 5-year ACS
Using FIPS code '41' for state 'OR'View(renter_bachelors)
# figure out decennial data, and join total population against renter occupied housing number, find % across the country
doctorates<- get_decennial(
geography = "block",
variables = "B15003_025",
year = 2020,
geometry = TRUE
)
View(doctorates)options(tigris_use_cache = TRUE)
#B25013_011
#Estimate!!Total:!!Renter-occupied housing units:!!Bachelor's degree or higher
#TENURE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER
or <- get_decennial(geography = "tract", variables = "B25013_011", year = 2020, state = "OR", geometry = TRUE)Getting data from the 2020 decennial Census
Using FIPS code '41' for state 'OR'
Using the PL 94-171 Redistricting Data Summary File
Error in UseMethod("gather") :
no applicable method for 'gather' applied to an object of class "character"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